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Record W2313754626 · doi:10.1021/nl201682h

Solar Cells Using Quantum Funnels

2011· article· en· W2313754626 on OpenAlexaff
Illan J. Kramer, Larissa Levina, Ratan Debnath, David Zhitomirsky, Edward H. Sargent

Bibliographic record

VenueNano Letters · 2011
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersKing Abdullah University of Science and Technology
KeywordsFunnelQuantum dotQuantumSolar cellQuantum point contactSemiconductorPhotoelectric effectOptoelectronicsAcceptorElectronNanotechnologyQuantum dot laserPhysicsMaterials scienceQuantum wellChemistryOpticsCondensed matter physicsQuantum mechanicsSemiconductor laser theory

Abstract

fetched live from OpenAlex

Colloidal quantum dots offer broad tuning of semiconductor bandstructure via the quantum size effect. Devices involving a sequence of layers comprised of quantum dots selected to have different diameters, and therefore bandgaps, offer the possibility of funneling energy toward an acceptor. Here we report a quantum funnel that efficiently conveys photoelectrons from their point of generation toward an intended electron acceptor. Using this concept we build a solar cell that benefits from enhanced fill factor as a result of this quantum funnel. This concept addresses limitations on transport in soft condensed matter systems and leverages their advantages in large-area optoelectronic devices and systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.224
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations132
Published2011
Admission routes1
Has abstractyes

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